Spillovers between hydrogen, nuclear, and AI sectors: The impact of climate policy uncertainty and geopolitical risks

This study investigates the spillover effects between hydrogen energy, nuclear energy, and artificial intelligence (AI) sectors in the context of the global clean energy transition, with a particular focus on the impact of climate policy uncertainty (CPU) and geopolitical risks (GPR). Employing the TVP-VAR extended joint connectedness approach, the findings show a high connectedness that indicates significant spillovers among these sectors. Hydrogen energy emerges as a dominant transmitter of shocks, reflecting its sensitivity to regulatory changes and fluctuating demand. However, nuclear energy acts as a stabilising force that offers hedging opportunities and resilience against market turbulence. The AI sector exhibits strong connectedness, primarily as a net receiver of shocks, driven by its dependency on clean energy sources and vulnerability to energy market volatility. Using the GARCH-MIDAS framework, the study identifies a temporal asymmetry in market responses to CPU and GPR. CPU triggers immediate but short-lived disruptions, while GPR induces delayed yet persistent effects that intensify cross-sector spillovers over time. These results underline the vulnerabilities of sectors reliant on regulatory clarity and geopolitical stability. This study provides practical insights for investors, policymakers, technology, and energy companies to better manage systemic risks at the crossroads of clean energy, technological innovation, and uncertainty. • Explores spillover effects between hydrogen energy, nuclear energy, and AI sectors. • Finds hydrogen energy drives shocks, while nuclear energy offers stability and resilience. • Shows CPU triggers short-term disruptions, while GPR causes persistent systemic risks. • Highlights AI reliance on clean energy and exposure to energy market uncertainties. • Offers insights for managing risks at the nexus of energy transitions and technological growth.